1% Generated by roxygen2: do not edit by hand 2% Please edit documentation in R/fitted.KFS.R 3\name{fitted.SSModel} 4\alias{fitted.SSModel} 5\alias{fitted.KFS} 6\title{Smoothed Estimates or One-step-ahead Predictions of Fitted Values} 7\usage{ 8\method{fitted}{KFS}(object, start = NULL, end = NULL, filtered = FALSE, ...) 9 10\method{fitted}{SSModel}(object, start = NULL, end = NULL, filtered = FALSE, nsim = 0, ...) 11} 12\arguments{ 13\item{object}{An object of class \code{KFS} or \code{SSModel}.} 14 15\item{start}{The start time of the period of interest. Defaults to first time 16point of the object.} 17 18\item{end}{The end time of the period of interest. Defaults to the last time 19point of the object.} 20 21\item{filtered}{Logical, return filtered instead of smoothed estimates of 22state vector. Default is \code{FALSE}.} 23 24\item{...}{Additional arguments to \code{\link{KFS}}. 25Ignored in method for object of class \code{KFS}.} 26 27\item{nsim}{Only for method for for non-Gaussian model of class \code{SSModel}. 28The number of independent samples used in importance sampling. 29Default is 0, which computes the 30approximating Gaussian model by \code{\link{approxSSM}} and performs the 31usual Gaussian filtering/smoothing so that the smoothed state estimates 32equals to the conditional mode of \eqn{p(\alpha_t|y)}{p(\alpha[t]|y)}. 33In case of \code{nsim = 0}, the mean estimates and their variances are computed using 34the Delta method (ignoring the covariance terms).} 35} 36\value{ 37Multivariate time series containing fitted values. 38} 39\description{ 40Computes fitted values from output of \code{KFS} 41(or using the \code{SSModel} object), i.e. one-step-ahead 42predictions \eqn{f(\theta_t | y_{t-1}, \ldots, y_1)}{ 43f(\theta[t] | y[t-1], ... , y[1]),} (\code{m}) or smoothed estimates 44\eqn{f(\theta_t | y_n, \ldots, y_1)}{f(\theta[t] | y[n], ... , y[1]),} (\code{muhat}), 45where \eqn{f} is the inverse of the link function 46(identity in Gaussian case), except in case of Poisson distribution where 47\eqn{f} is multiplied with the exposure \eqn{u_t}{u[t]}. 48} 49\examples{ 50data("sexratio") 51model <- SSModel(Male ~ SSMtrend(1,Q = list(NA)),u = sexratio[, "Total"], 52 data = sexratio, distribution = "binomial") 53model <- fitSSM(model,inits = -15, method = "BFGS")$model 54out <- KFS(model) 55identical(drop(out$muhat), fitted(out)) 56 57fitted(model) 58} 59\seealso{ 60\code{\link{signal}} for partial signals and their covariances. 61} 62